307 Final
Bre Jordan
2025-12-17
#CLEANING + ORGANIZING DATA ###Pulling data Duplicate responses were removed in qualtrics as they were all blank forms, likely resulting from students
myData<- read.csv("~/Documents/R Folder/QAC307+Project_December+17,+2025_10.07.csv", na.strings = c("", "NA"))###Cleaning data I filtered the data to remove data with no responses. Since the question order was randomized, I did this by verifiying that there was no response in the first section, as observations with no responses in the first section were blank in all other sections as well.
myData <- myData[c(-1,-2), ]
myData %>%
select(-Emails, -Introduction_DO, -Attention.Check_DO, -Status, -Duration..in.seconds., -ResponseId, -DistributionChannel, -RecordedDate, -UserLanguage, -Q_DuplicateRespondent) ->myData
myData %>%
filter(if_any(c(Intentions_1, Intentions_2, Intentions_3, Intentions_4, Intentions_5, Intentions_6, Intentions_7),
~ !is.na(.)))->myData###Renaming Variables A separate group data project I was working on concurrently uses a package regularly creates issues with dplyr by overwriting which is why it’s specifically called each time.
myData %>%
dplyr::rename(Petition_Month=Intentions_1,
Petition_Easy=Intentions_2,
Petition_Hard=Intentions_3,
Donate_7days=Intentions_4,
Donate_Easy=Intentions_5,
Donate_Hard=Intentions_6,
Help_Others=Intentions_7) -> myData
myData %>%
dplyr::rename(OneSign_Helps=Beliefs_1,
Signing_Easy=Beliefs_2,
Signing_Important=Beliefs_3,
OneDonate_Helps=Beliefs_4,
Donating_Easy=Beliefs_5,
Donating_Important=Beliefs_6,
Change_Large=Beliefs_7,
Change_Small=Beliefs_8) -> myData
myData$Q21[myData$Q21_6_TEXT=="2027 on medical leave"]<-"2027"
myData$Q21[myData$Q21 %in% c("Grad", "Other")] <- "Grad/Other"
myData %>%
dplyr::rename(Gender=Q22,
Year=Q21) -> myData
myData$Q19_1 <- factor(myData$Q19_1, levels = c("2", "3", "4", "5"), ordered=TRUE)##Cleaning Variables
#Intentions
myData$Petition_Month <- as.numeric(myData$Petition_Month)
myData$Petition_Easy <- as.numeric(myData$Petition_Easy)
myData$Petition_Hard <- as.numeric(myData$Petition_Hard)
myData$Donate_7days <- as.numeric(myData$Donate_7days)
myData$Donate_Easy <- as.numeric(myData$Donate_Easy)
myData$Donate_Hard <- as.numeric(myData$Donate_Hard)
myData$Help_Others <- as.numeric(myData$Help_Others)
#Beliefs
myData$OneSign_Helps <- as.numeric(myData$OneSign_Helps)
myData$Signing_Easy <- as.numeric(myData$Signing_Easy)
myData$Signing_Important <- as.numeric(myData$Signing_Important)
myData$OneDonate_Helps <- as.numeric(myData$OneDonate_Helps)
myData$Donating_Easy <- as.numeric(myData$Donating_Easy)
myData$Donating_Important <- as.numeric(myData$Donating_Important)
myData$Change_Large <- as.numeric(myData$Change_Large)
myData$Change_Small <- as.numeric(myData$Change_Small)###Building Composites
#Petition Composite Intentions
myData <- myData %>%
mutate(
Petition_Composite = case_when(
!is.na(Petition_Month) & !is.na(Petition_Easy) & !is.na(Petition_Hard) ~
(Petition_Month + Petition_Easy + Petition_Hard) / 3,
!is.na(Petition_Month) & !is.na(Petition_Easy) ~
(Petition_Month + Petition_Easy) / 2,
!is.na(Petition_Month) & !is.na(Petition_Hard) ~
(Petition_Month + Petition_Hard) / 2,
!is.na(Petition_Easy) & !is.na(Petition_Hard) ~
(Petition_Easy + Petition_Hard) / 2))
#Donation Composite Intentions
myData <- myData %>%
mutate(
Donation_Composite = case_when(
!is.na(Donate_7days) & !is.na(Donate_Easy) & !is.na(Donate_Hard) ~
(Donate_7days + Donate_Easy + Donate_Hard) / 3,
!is.na(Donate_7days) & !is.na(Donate_Easy) ~
(Donate_7days + Donate_Easy) / 2,
!is.na(Donate_7days) & !is.na(Donate_Hard) ~
(Donate_7days + Donate_Hard) / 2,
!is.na(Donate_Easy) & !is.na(Donate_Hard) ~
(Donate_Easy + Donate_Hard) / 2))
#Petition Belief Composite
myData <- myData %>%
mutate(
Petition_Belief_Composite = case_when(
!is.na(OneSign_Helps) & !is.na(Signing_Easy) & !is.na(Signing_Important) ~
(OneSign_Helps + Signing_Easy + Signing_Important) / 3,
!is.na(OneSign_Helps) & !is.na(Signing_Easy) ~
(OneSign_Helps + Signing_Easy) / 2,
!is.na(OneSign_Helps) & !is.na(Signing_Important) ~
(OneSign_Helps + Signing_Important) / 2,
!is.na(Signing_Easy) & !is.na(Signing_Important) ~
(Signing_Easy + Signing_Important) / 2))
#Donation Belief Composite
myData <- myData %>%
mutate(
Donation_Belief_Composite = case_when(
!is.na(OneDonate_Helps) & !is.na(Donating_Easy) & !is.na(Donating_Important) ~
(OneDonate_Helps + Donating_Easy + Donating_Important) / 3,
!is.na(OneDonate_Helps) & !is.na(Donating_Easy) ~
(OneDonate_Helps + Donating_Easy) / 2,
!is.na(OneDonate_Helps) & !is.na(Donating_Important) ~
(OneDonate_Helps + Donating_Important) / 2,
!is.na(Donating_Easy) & !is.na(Donating_Important) ~
(Donating_Easy + Donating_Important) / 2))###Manipulating Vignettes_DO
#shortening Vignettes_DO
myData$Vignettes[myData$Vignettes_DO=="Instructions|Petitions - Sustain"]<-"Petition - Sustain"
myData$Vignettes[myData$Vignettes_DO=="Instructions|Petitions - Change"]<-"Petition - Change"
myData$Vignettes[myData$Vignettes_DO=="Instructions|Donations - Sustain"]<-"Donation - Sustain"
myData$Vignettes[myData$Vignettes_DO=="Instructions|Donations - Change"]<-"Donation - Change"
#factoring + releveling for analysis
myData$Vignettes<-factor(myData$Vignettes,
levels=c("Petition - Change", "Donation - Change",
"Petition - Sustain", "Donation - Sustain"),
ordered=F)
#collapsing by talk type
myData$Type[myData$Vignettes %in% c("Petition - Change", "Donation - Change")] <- "Change"
myData$Type[myData$Vignettes %in% c("Petition - Sustain", "Donation - Sustain")] <- "Sustain"
#collapsing by action mode
myData$Mode[myData$Vignettes %in% c("Petition - Change", "Petition - Sustain")] <- "Petition"
myData$Mode[myData$Vignettes %in% c("Donation - Change", "Donation - Sustain")] <- "Donation"###Creating Subsets
myData %>%
filter(Vignettes_DO %in% c("Instructions|Petitions - Sustain", "Instructions|Petitions - Change"))->myData_Petition
myData %>%
filter(Vignettes_DO %in% c("Instructions|Donations - Sustain", "Instructions|Donations - Change"))->myData_Donation#FREQ TABLES
freq(myData$Vignettes)
## myData$Vignettes
## Frequency Percent
## Petition - Change 26 21.67
## Donation - Change 32 26.67
## Petition - Sustain 35 29.17
## Donation - Sustain 27 22.50
## Total 120 100.00freq(myData$Type)
## myData$Type
## Frequency Percent
## Change 58 48.33
## Sustain 62 51.67
## Total 120 100.00freq(myData$Mode)
## myData$Mode
## Frequency Percent
## Donation 59 49.17
## Petition 61 50.83
## Total 120 100.00freq(myData$Petition_Month)
## myData$Petition_Month
## Frequency Percent Valid Percent
## 1 8 6.667 7.339
## 2 34 28.333 31.193
## 3 20 16.667 18.349
## 4 25 20.833 22.936
## 5 22 18.333 20.183
## NA's 11 9.167
## Total 120 100.000 100.000freq(myData$Petition_Easy)
## myData$Petition_Easy
## Frequency Percent Valid Percent
## 1 6 5.000 5.505
## 2 23 19.167 21.101
## 3 20 16.667 18.349
## 4 27 22.500 24.771
## 5 33 27.500 30.275
## NA's 11 9.167
## Total 120 100.000 100.000freq(myData$Petition_Hard)
## myData$Petition_Hard
## Frequency Percent Valid Percent
## 1 2 1.667 2.353
## 2 25 20.833 29.412
## 3 26 21.667 30.588
## 4 20 16.667 23.529
## 5 12 10.000 14.118
## NA's 35 29.167
## Total 120 100.000 100.000freq(myData$Donate_7days)
## myData$Donate_7days
## Frequency Percent Valid Percent
## 1 6 5.00 6.897
## 2 40 33.33 45.977
## 3 26 21.67 29.885
## 4 9 7.50 10.345
## 5 6 5.00 6.897
## NA's 33 27.50
## Total 120 100.00 100.000freq(myData$Donate_Easy)
## myData$Donate_Easy
## Frequency Percent Valid Percent
## 1 7 5.833 7.955
## 2 32 26.667 36.364
## 3 28 23.333 31.818
## 4 14 11.667 15.909
## 5 7 5.833 7.955
## NA's 32 26.667
## Total 120 100.000 100.000freq(myData$Donate_Hard)
## myData$Donate_Hard
## Frequency Percent Valid Percent
## 1 17 14.167 25.758
## 2 24 20.000 36.364
## 3 17 14.167 25.758
## 4 6 5.000 9.091
## 5 2 1.667 3.030
## NA's 54 45.000
## Total 120 100.000 100.000freq(myData$Help_Others)
## myData$Help_Others
## Frequency Percent Valid Percent
## 1 3 2.500 2.542
## 2 9 7.500 7.627
## 3 21 17.500 17.797
## 4 45 37.500 38.136
## 5 40 33.333 33.898
## NA's 2 1.667
## Total 120 100.000 100.000freq(myData$OneSign_Helps)
## myData$OneSign_Helps
## Frequency Percent Valid Percent
## 2 20 16.67 18.02
## 3 43 35.83 38.74
## 4 35 29.17 31.53
## 5 13 10.83 11.71
## NA's 9 7.50
## Total 120 100.00 100.00freq(myData$Signing_Easy)
## myData$Signing_Easy
## Frequency Percent Valid Percent
## 2 8 6.667 6.838
## 3 20 16.667 17.094
## 4 39 32.500 33.333
## 5 50 41.667 42.735
## NA's 3 2.500
## Total 120 100.000 100.000freq(myData$Signing_Important)
## myData$Signing_Important
## Frequency Percent Valid Percent
## 2 14 11.667 12.17
## 3 32 26.667 27.83
## 4 51 42.500 44.35
## 5 18 15.000 15.65
## NA's 5 4.167
## Total 120 100.000 100.00freq(myData$OneDonate_Helps)
## myData$OneDonate_Helps
## Frequency Percent Valid Percent
## 1 3 2.500 2.655
## 2 23 19.167 20.354
## 3 44 36.667 38.938
## 4 23 19.167 20.354
## 5 20 16.667 17.699
## NA's 7 5.833
## Total 120 100.000 100.000freq(myData$Donating_Easy)
## myData$Donating_Easy
## Frequency Percent Valid Percent
## 1 1 0.8333 0.9091
## 2 29 24.1667 26.3636
## 3 34 28.3333 30.9091
## 4 31 25.8333 28.1818
## 5 15 12.5000 13.6364
## NA's 10 8.3333
## Total 120 100.0000 100.0000freq(myData$Donating_Important)
## myData$Donating_Important
## Frequency Percent Valid Percent
## 2 13 10.833 11.21
## 3 37 30.833 31.90
## 4 38 31.667 32.76
## 5 28 23.333 24.14
## NA's 4 3.333
## Total 120 100.000 100.00freq(myData$Change_Large)
## myData$Change_Large
## Frequency Percent Valid Percent
## 1 11 9.167 10.476
## 2 48 40.000 45.714
## 3 28 23.333 26.667
## 4 10 8.333 9.524
## 5 8 6.667 7.619
## NA's 15 12.500
## Total 120 100.000 100.000freq(myData$Change_Small)
## myData$Change_Small
## Frequency Percent
## 1 1 0.8333
## 2 5 4.1667
## 3 17 14.1667
## 4 39 32.5000
## 5 58 48.3333
## Total 120 100.0000freq(myData$Petition_Composite)
## myData$Petition_Composite
## Frequency Percent Valid Percent
## 1 3 2.5000 2.8302
## 1.5 3 2.5000 2.8302
## 1.66666666666667 2 1.6667 1.8868
## 2 15 12.5000 14.1509
## 2.33333333333333 5 4.1667 4.7170
## 2.5 1 0.8333 0.9434
## 2.66666666666667 10 8.3333 9.4340
## 3 8 6.6667 7.5472
## 3.33333333333333 9 7.5000 8.4906
## 3.5 4 3.3333 3.7736
## 3.66666666666667 12 10.0000 11.3208
## 4 9 7.5000 8.4906
## 4.33333333333333 6 5.0000 5.6604
## 4.66666666666667 8 6.6667 7.5472
## 5 11 9.1667 10.3774
## NA's 14 11.6667
## Total 120 100.0000 100.0000freq(myData$Donation_Composite)
## myData$Donation_Composite
## Frequency Percent Valid Percent
## 1 3 2.5000 3.571
## 1.33333333333333 2 1.6667 2.381
## 1.5 3 2.5000 3.571
## 1.66666666666667 5 4.1667 5.952
## 2 18 15.0000 21.429
## 2.33333333333333 8 6.6667 9.524
## 2.5 3 2.5000 3.571
## 2.66666666666667 14 11.6667 16.667
## 3 13 10.8333 15.476
## 3.33333333333333 1 0.8333 1.190
## 3.66666666666667 3 2.5000 3.571
## 4 5 4.1667 5.952
## 4.33333333333333 4 3.3333 4.762
## 5 2 1.6667 2.381
## NA's 36 30.0000
## Total 120 100.0000 100.000freq(myData$Petition_Belief_Composite)
## myData$Petition_Belief_Composite
## Frequency Percent Valid Percent
## 2 2 1.667 1.754
## 2.33333333333333 2 1.667 1.754
## 2.66666666666667 5 4.167 4.386
## 3 16 13.333 14.035
## 3.33333333333333 14 11.667 12.281
## 3.66666666666667 26 21.667 22.807
## 4 19 15.833 16.667
## 4.33333333333333 16 13.333 14.035
## 4.66666666666667 8 6.667 7.018
## 5 6 5.000 5.263
## NA's 6 5.000
## Total 120 100.000 100.000freq(myData$Donation_Belief_Composite)
## myData$Donation_Belief_Composite
## Frequency Percent Valid Percent
## 1.66666666666667 1 0.8333 0.8772
## 2 3 2.5000 2.6316
## 2.33333333333333 4 3.3333 3.5088
## 2.5 1 0.8333 0.8772
## 2.66666666666667 22 18.3333 19.2982
## 3 13 10.8333 11.4035
## 3.33333333333333 18 15.0000 15.7895
## 3.66666666666667 20 16.6667 17.5439
## 4 11 9.1667 9.6491
## 4.33333333333333 11 9.1667 9.6491
## 4.66666666666667 2 1.6667 1.7544
## 5 8 6.6667 7.0175
## NA's 6 5.0000
## Total 120 100.0000 100.0000#covariate for ANCOVAs
freq(myData$Q19_1) 
## myData$Q19_1
## Frequency Percent Valid Percent Cum Percent
## 2 30 25.000 27.27 27.27
## 3 40 33.333 36.36 63.64
## 4 25 20.833 22.73 86.36
## 5 15 12.500 13.64 100.00
## NA's 10 8.333
## Total 120 100.000 100.00#demographics
freq(myData$Year) 
## myData$Year
## Frequency Percent Valid Percent
## 2026 24 20.000 20.690
## 2027 36 30.000 31.034
## 2028 23 19.167 19.828
## 2029 27 22.500 23.276
## Grad/Other 6 5.000 5.172
## NA's 4 3.333
## Total 120 100.000 100.000freq(myData$Gender) 
## myData$Gender
## Frequency Percent
## Man 35 29.167
## Non-binary / Genderqueer/ Gender Non-conforming / Other 17 14.167
## Prefer not to say 5 4.167
## Woman 58 48.333
## NA's 5 4.167
## Total 120 100.000
## Valid Percent
## Man 30.435
## Non-binary / Genderqueer/ Gender Non-conforming / Other 14.783
## Prefer not to say 4.348
## Woman 50.435
## NA's
## Total 100.000# used in paper
freq(myData$Attention.Check) 
## myData$Attention.Check
## Frequency Percent Valid Percent
## Data 4 3.333 3.67
## Donations 42 35.000 38.53
## Petitions 63 52.500 57.80
## NA's 11 9.167
## Total 120 100.000 100.00#GRAPHS
ggplot(myData)+
geom_boxplot(aes(x = Type, y = Petition_Composite, fill = Type), alpha = 0.5) +
facet_grid(.~ Mode) +
scale_fill_manual(values = c("Sustain" = "gold3","Change" = "darkslategray3"))+
xlab("Vignette Type") +
ylab("Petition Intention Composite Score")+
ggtitle("Petition Intention according to Vignette Type and Mode")## Warning: Removed 14 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
ggplot(myData)+
geom_boxplot(aes(x = Type, y = Donation_Composite, fill = Type), alpha = 0.5) +
facet_grid(.~ Mode) +
scale_fill_manual(values = c("Sustain" = "gold3","Change" = "darkslategray3"))+
xlab("Vignette Type") +
ylab("Donation Intention Composite Score")+
ggtitle("Donation Intention according to Vignette Type and Mode")## Warning: Removed 36 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
ggplot(myData)+
geom_boxplot(aes(x = Type, y = Petition_Belief_Composite, fill = Type), alpha = 0.5) +
facet_grid(.~ Mode) +
scale_fill_manual(values = c("Sustain" = "gold3","Change" = "darkslategray3"))+
xlab("Vignette Type") +
ylab("Petition Belief Composite Score")+
ggtitle("Petition Belief according to Vignette Type and Mode")## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
ggplot(myData)+
geom_boxplot(aes(x = Type, y = Donation_Belief_Composite, fill = Type), alpha = 0.5) +
facet_grid(.~ Mode) +
scale_fill_manual(values = c("Sustain" = "gold3","Change" = "darkslategray3"))+
xlab("Vignette Type") +
ylab("Donation Belief Composite Score")+
ggtitle("Donation Belief according to Vignette Type and Mode")## Warning: Removed 6 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
#ANALYSIS ### Base Analysis of Composites and Vignettes
tapply(myData$Petition_Composite, myData$Vignettes, mean, na.rm = TRUE)## Petition - Change Donation - Change Petition - Sustain Donation - Sustain
## 3.293651 3.458333 3.131313 3.277778model1 <- aov(Petition_Composite~Vignettes, data=myData)
summary(model1)## Df Sum Sq Mean Sq F value Pr(>F)
## Vignettes 3 1.62 0.541 0.445 0.721
## Residuals 102 124.00 1.216
## 14 observations deleted due to missingnessplot(model1)
tapply(myData$Donation_Composite, myData$Vignettes, mean, na.rm = TRUE)## Petition - Change Donation - Change Petition - Sustain Donation - Sustain
## 2.803922 2.695652 2.347222 2.633333model2 <- aov(Donation_Composite~Vignettes, data=myData)
summary(model2)## Df Sum Sq Mean Sq F value Pr(>F)
## Vignettes 3 2.47 0.8241 1.048 0.376
## Residuals 80 62.91 0.7864
## 36 observations deleted due to missingnessplot(model2)
#ANCOVA
model3 <- aov(Petition_Composite~Vignettes+Q19_1, data=myData)
summary(model3)## Df Sum Sq Mean Sq F value Pr(>F)
## Vignettes 3 1.72 0.573 0.559 0.64321
## Q19_1 3 15.37 5.124 5.002 0.00295 **
## Residuals 91 93.21 1.024
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 22 observations deleted due to missingnessplot(model3)
model4 <- aov(Donation_Composite~Vignettes+Q19_1, data=myData)
summary(model4)## Df Sum Sq Mean Sq F value Pr(>F)
## Vignettes 3 2.96 0.9865 1.185 0.322
## Q19_1 3 2.07 0.6915 0.830 0.482
## Residuals 71 59.13 0.8328
## 42 observations deleted due to missingnessplot(model4)
#Analysis of Beliefs
tapply(myData$Petition_Belief_Composite, myData$Vignettes, mean, na.rm = TRUE)## Petition - Change Donation - Change Petition - Sustain Donation - Sustain
## 3.813333 3.620690 3.637255 3.871795model5 <- aov(Petition_Belief_Composite~Vignettes+Q19_1, data=myData)
summary(model5)## Df Sum Sq Mean Sq F value Pr(>F)
## Vignettes 3 0.98 0.3280 0.698 0.556
## Q19_1 3 0.93 0.3090 0.657 0.580
## Residuals 98 46.08 0.4703
## 15 observations deleted due to missingnessplot(model5)
tapply(myData$Donation_Belief_Composite, myData$Vignettes, mean, na.rm = TRUE)## Petition - Change Donation - Change Petition - Sustain Donation - Sustain
## 3.546667 3.189655 3.529412 3.474359model6 <- aov(Donation_Belief_Composite~Vignettes+Q19_1, data=myData)
summary(model6)## Df Sum Sq Mean Sq F value Pr(>F)
## Vignettes 3 1.93 0.6449 1.086 0.359
## Q19_1 3 2.15 0.7180 1.209 0.311
## Residuals 98 58.20 0.5938
## 15 observations deleted due to missingnessplot(model6)
###Analysis of Petition and Donation by Type
model7 <- aov(Petition_Composite~Type+Q19_1, data=myData)
summary(model7)## Df Sum Sq Mean Sq F value Pr(>F)
## Type 1 1.50 1.504 1.484 0.22617
## Q19_1 3 14.59 4.863 4.801 0.00374 **
## Residuals 93 94.21 1.013
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 22 observations deleted due to missingnessplot(model7)
model8 <- aov(Donation_Composite~Type, data=myData)
summary(model8)## Df Sum Sq Mean Sq F value Pr(>F)
## Type 1 1.46 1.4647 1.879 0.174
## Residuals 82 63.92 0.7795
## 36 observations deleted due to missingnessplot(model8)
###Order Effects
used_rows <- as.numeric(rownames(model1$model))
graphdata<- data.frame(EndDate = myData$EndDate[used_rows],
Residuals = model1$residuals)
graphdata$EndDate<-as.Date(graphdata$EndDate)
ggplot(data=graphdata)+
geom_point(aes(x=EndDate, y=Residuals))+
ylab("Residuals")+
xlab("End Date")+
ggtitle("Examining Order Effects")
Exploratory
model9 <- aov(Petition_Composite~Vignettes+Q19_1+Gender+Year, data=myData_Donation)
summary(model9)## Df Sum Sq Mean Sq F value Pr(>F)
## Vignettes 1 0.338 0.338 0.421 0.520468
## Q19_1 3 16.246 5.415 6.744 0.000928 ***
## Gender 3 4.483 1.494 1.861 0.152643
## Year 4 4.848 1.212 1.509 0.218820
## Residuals 38 30.512 0.803
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 9 observations deleted due to missingnessplot(model9)
model10 <- aov(Donation_Composite~Vignettes+Q19_1+Gender+Year, data=myData_Petition)
summary(model10)## Df Sum Sq Mean Sq F value Pr(>F)
## Vignettes 1 2.701 2.7006 3.639 0.0685 .
## Q19_1 3 6.730 2.2432 3.023 0.0493 *
## Gender 3 4.021 1.3403 1.806 0.1730
## Year 4 1.865 0.4663 0.628 0.6469
## Residuals 24 17.810 0.7421
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 25 observations deleted due to missingnessplot(model10)## Warning: not plotting observations with leverage one:
## 27
